Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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BIOMETRIC MATCHING SYSTEM
This application claims priority based on European
Patent Application 11306347.3 entitled "BIOMETRIC MATCHING
SYSTEM" filed October 18, 2011.
FIELD
The present disclosure relates to identifying a record
in a biometric database based on an input biometric sample.
BACKGROUND
The use of biometric data for the identification of
individuals is increasingly becoming the preferred choice in
many environments due to the relative difficulty in fraudulently
replicating the data. For example, due to increasing fraud
involving payment cards such as credit cards, it has been
proposed to use biometric data, such as for example
fingerprints, to identify customers in shops or supermarkets to
allow a payment transaction to be initiated. As a further
example, biometric data is increasing used for identifying
individuals authorized to enter restricted areas, such as, for
example, gyms, apartment blocks or vehicles, or to pass through
a border control. Furthermore, criminal databases have long been
used for identifying individuals based on biometric data, such
as, for example, a fingerprint or facial image taken at a crime
scene.
To identify individuals, a biometric sample is obtained
and compared to the records of a database, until a match is
found. In the majority of applications, speed is of the essence.
For example, if a user is at the checkout of a supermarket, or
at a border control gate, an identification delay of more than
several seconds may be considered unacceptable. A further
requirement is that there are very few errors, i.e. very few
false positive and false negative results. Indeed, if a customer
at the checkout of a supermarket can not be identified, or is
wrongly identified, this could lead to the customer being unable
to make the payment, or to the wrong person being billed.
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However, there is at least one technical problem in
increasing the speed of identification and/or in reducing the
error rate in current biometric identification systems.
SUMMARY
It is an aim of embodiments of the present disclosure
to at least partially address one or more problems in the prior
art.
According to one aspect, there is provided a method of
identifying a biometric record of an individual in a database
comprising a plurality of biometric records, each record
comprising at least one reference biometric sample, the method
comprising: receiving, by a biometric identification unit, an
input biometric sample with associated source information;
selecting, by the biometric identification unit using a
reference table, and based on said source infolmation a matching
process; and applying by said biometric identification unit said
selected matching process to at least some of said biometric
records of said database to determine whether said input
biometric sample matches a reference biometric sample of one of
said biometric records. For example, selecting the matching
process comprises selecting the matching process to be applied
to each of said at least some biometric records of the database.
According to one embodiment, selecting said matching
process comprises at least selecting a filtering threshold used
for eliminating records of said database.
According to another embodiment, the method further
comprises assigning an amount of processing resources to said
matching process based on said source information, the amount of
processing resources determining the processing time of said
matching process.
According to another embodiment, the method further
comprises, prior to applying said selected matching process,
selecting, based on said source information, records of said
database to which said matching process is to be applied.
According to another embodiment, the method further
comprises extracting a quality value from said input biometric
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sample, wherein a filtering threshold for eliminating records
during said selected matching process is selected based on said
quality value.
According to another embodiment, selecting the matching
process comprises either: selecting a filtering algorithm used
for eliminating records of said database; or selecting a
filtering threshold used for eliminating records of said
database; or selecting the type of biometric sample used for
eliminating records of said database; or selecting the amount of
processing resources to be used for eliminating records from
said database; or a combination of any of the above.
According to another embodiment, the method further
comprises initiating by said biometric identification unit an
electronic payment based on a result of said matching process.
According to another embodiment, the method further
comprises, prior to receiving said input biometric sample,
enrolling said individual by adding a new record containing at
least one reference biometric sample of said individual to said
database.
According to another embodiment, the method further
comprises associating in said database said new record with at
least two source apparatuses.
According to a further aspect, there is provided a
biometric identification system comprising: a database
comprising a plurality of biometric records, each record
comprising at least one reference biometric sample; an input for
receiving an input biometric sample with associated source
information; and a biometric identification unit configured to:
select, using a reference table and based on said source
information a matching process; and apply said selected matching
process to said biometric records of said database to determine
whether said input biometric sample matches a reference
biometric sample of one of said biometric records.
According to one embodiment, said input is in
communication with at least two remote source apparatuses each
comprising a biometric capturing device.
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According to another embodiment, each of said at least
two source apparatuses is either: a merchant payment terminal;
or an entry system to a restricted area; or a border control
gate; or a combination of any of the above.
According to another embodiment, said biometric
identification unit is configured to apply said selected
matching process to the records of said database that are
associated with one of said source apparatuses as indicated by
said source information.
According to another embodiment, the system further
comprises a lookup table indicating a link between each record
of said database and payment account details.
According to another embodiment, said payment account
details include payment information to enable a payment to be
initiated.
The details of various embodiments are set forth in the
accompanying drawings and the description below. Other potential
features will became apparent from the description, the drawings and
the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing and other purposes, features and aspects
of the disclosure will become apparent from the following
detailed description of example embodiments, given by way of
illustration and not limitation with reference to the
accompanying drawings, in which:
Figure 1 schematically illustrates a biometric
identification system;
Figure 2A schematically illustrates a biometric
identification system according to an example embodiment;
Figure 2B schematically illustrates a biometric
identification unit of Figure 2A in more detail according to an
example embodiment;
Figure 3 illustrates a portion of a biometric database
according to an example embodiment;
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Figure 4 is a flow diagram illustrating operations in a
method of identifying a biometric record according to an example
embodiment;
Figure 5 schematically illustrates the biometric
identification system of Figure 2A in more detail according to
an example embodiment;
Figure 6A is a flow diagram showing operations in a
method of registering a new user of a biometric identification
system;
Figure 6B is a flow diagram showing operations in a
method of identifying the new user during a transaction
according to example embodiments;
Figure 7 is a flow diagram showing operations of a
matching process according to an example embodiment;
Figures 8A and 8B illustrate examples of extracted
metadata according to an example embodiment.
Throughout the figures, like features have been
labelled with like reference numerals.
DETAILED DESCRIPTION
Figure 1 illustrates a biometric identification system
100 comprising a matching engine 102 for identifying a record
matching an input biometric sample from a single source
apparatus 104. As used herein, "source apparatus" designates one
or more devices that provide biometric samples and allow access
to products, to a restricted area, or to other type of services,
if an authorized record holder is identified. For example, the
source apparatus 104 could correspond to a cashier teLminal in a
supermarket, or an entry barrier at an airport lounge.
Matching engine 102 receives a biometric input sample
SB from a biometric capturing device (BCD) 106 of the source
apparatus 104. The biometric capturing device 106 is for example
a visible light or infra-red camera, a fingerprint sensor,
microphone or any other detector suitable for capturing a
biometric sample of an individual. Input biometric sample SB
could for example be a photo of the face, a fingerprint, an iris
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scan, an image of a signature, a finger vein or palm vein image,
a voice sample, or any other fom of biometric data.
In some cases the individual is present at the
capturing device 106 and submits the biometric input sample, for
example by presenting their face to a camera or placing a finger
on a fingeLprint detector. In other cases, the biometric data
may be retrieved from another source, such as from the scene of
a crime in the case of a fingerprint, or from a surveillance
video image.
Engine 102 has access to a biometric database (DB) 108
storing biometric records each associated with one or more
reference biometric samples. A biometric sample is defined
herein as data in the form of an image and/or biometric template
based on the image, representing biometric data of an
individual. Engine 102 searches the biometric database for a
record having a reference biometric sample matching the input
biometric sample. A result R is provided on an output 110 to
receiving equipment 112 of the source apparatus 104. The result
R for example simply indicates whether or not a match was found,
or may also contain data associated with the matching record,
such as a reference number of the matching record, the identity,
such as name, of the individual associated with the matching
record, or other data. The receiving equipment 112 is for
example any equipment that reacts to the result R of the
matching process to provide or deny access to one or more
products, restricted areas or services.
The matching engine 102 of the biometric identification
system 100 of Figure 1 is programmed to apply certain matching
processes in order to provide the result R within a certain time
delay and with a desired accuracy, in other words with a given
percentage of false positive or false negative matches. However,
the time delay and accuracy requirements may change, and the
accuracy of the matching engine 102 may evolve in time due for
example to a degradation of the biometric capturing device 106.
Figure 2A illustrates an alternative biometric
identification system 200 comprising a biometric identification
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unit 102 for identifying a record matching an input biometric
sample from two different source apparatuses 204A and 204B, via
a communications channel 205. In alternative embodiments there
may be more than two source apparatuses. Communications channel
205 could for example be a wired and/or wireless connection
comprising a local area network (LAN), a metropolitan data
network (MAN), wide area network (WAN) and/or the internet. Thus
the biometric identification unit 202 provides a centralized
identification service for a plurality of source
apparatuses/clients. Such an arrangement may for example be
referred to as an "in the cloud" approach.
The source apparatuses 204A, 204B comprise biometric
capturing devices 206A, 206B respectively, which provide
respective input biometric samples Sin and 5B2 to the biometric
identification unit 202. While each source apparatus 204A, 204B
has been illustrated with a single capturing device 206A, 206B,
each may comprise one or more additional biometric capturing
devices, which for example allow a matching record to be
identified based on more than one biometric sample.
The biometric identification unit 202 has access to a
common database (CDB) 208, which stores biometric records for
both the users registered to access the products of services
offered by source apparatus 204A and those registered to access
the products or services offered by source apparatus 204B. As
will be explained in more detail below, there may be some
overlap, meaning that some users may be registered to use both
apparatuses 204A, 204B.
The biometric identification unit 202 also has access
to a reference table 210, which for example indicates which
matching process is to be used for requests originating from
each of the source apparatuses 204A, 204B, and may also indicate
the level of service to be offered, in terms of speed and/or
accuracy.
For example, each of the entities associates with the
source apparatuses 204A, 2043 pays for a certain level of
service, such that the input biometric samples submitted by
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their respective capturing devices 206A, 206B for identification
are processed with a certain accuracy and/or at a certain speed.
In some cases, the cost may also be based on the number of
records that must be searched, and/or the quality of the
biometric samples provided by the capturing devices 206A, 206B.
The quality of a sample may be defined in any of a number of
ways, such as by the sharpness, contrast or size of an image, as
will be described in more detail below with reference to Figures
8A and 8B.
As one example, the source equipment 204A could
correspond to a border control gate that automatically
identifies individuals based on a facial image. As such, it may
be desirable that the matching process has very few false
positive matches, and is performed very quickly on a very large
number of potentially matching records. The source equipment
204B is for example a cashier terminal at a point of sale in a
shop or supermarket, allowing customers to use a fingerprint
detector to initiate a payment for their purchases. There may be
relatively few customers registered with this payment service,
and thus few records may have to be searched, and a longer
matching delay may be permissible when compared to the
requirements in the border control example. Thus the reference
table 210 for example indicates that, for biometric samples
originating from source apparatus 204A, a high level of
processing resources/priority is to be applied in order to
achieve a fast result, and a matching process is to be used that
results in very few false positives. On the other hand, the
reference table may indicate that relatively low level of
processing resources/priority is to be applied to records
originating from source apparatus 204B, and but that a matching
process with very few false positive and false negative results
is to be applied.
The results R1 and R2 from the matching processes
applied to the input biometric samples SBI and SB2 respectively
are provided to corresponding receiving equipment 212A, 212B of
the source apparatus 204A, 204B respectively.
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The matching process used for identifying a matching
biometric record in the biometric database 208 may comprise one
or more matching operations. A matching operation is one that
compares an input biometric sample to at least one reference
biometric sample of one or more records in the biometric
databases in order to determine a similarity score that is used
to identify a matching record.
Figure 2B illustrates the biometric identification unit
202 of Figure 2A in more detail according to one example. As
illustrated, the unit 202 comprises one or more processors 220
that may operate in parallel under the control of one or more
instruction memories 222. The processors 220 may comprise one or
more microprocessors, microcontrollers, digital signal
processors, or appropriate combinations thereof, and executes
instructions stored in the instruction memory or memories 222,
which could be a volatile memory such as a DRAM (dynamic random
access memory), or another type of memory.
The processors 220 are also in communication via an
interface 223 with one or more memory devices, for example
comprising non-volatile memories, such as hard disk drives or
FLASH drives (not illustrated in Figure 23) that store the
common biometric database 208, reference table 210, and may also
store other databases described in more detail below.
A display 224, as well as one or more input devices 226
such as a keyboard or mouse, may be provided for allowing an
administrator to control the operations of the biometric
identification unit 202, for example to download software
updntes, etc.
A communications interface 228 for example provides a
connection to the source apparatuses 204A, 204B, and in
particular to the biometric capturing devices 206A, 206B and
receiving devices 212A, 2123, via the communications channel 205
described above.
Figure 3 illustrates a portion of the common biometric
database 208 of Figure 2A, storing biometric records according
to an example embodiment. Each record of the database
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corresponds to a different individual. The particular record
holders of the records stored in the database 208 will depend on
with source apparatus 204A, 2042 they are registered with, and
could correspond to members of a gym, customers of a shop or
supermarket, employees of an office, travellers wishing to cross
a border, or other types of record holders.
In Figure 3, three biometric records are shown as an
example, having references "0001", "0002" and "0003"
respectively indicated in a field 302. Of course in practise the
database is likely to contain hundreds or thousands of records.
Each biometric record is associated with a corresponding record
holder, but for security reasons, the database 208 for example
only identifies these individuals by a reference number. A
separate table, for example stored by the biometric
identification unit 202, may indicate the mapping between the
reference numbers of field 302 and biographic information of the
corresponding record holder, such as name, address, account
details etc., depending on the application.
A field 304 for example comprises a digital image of
the face of the record holder, a field 306 for example comprises
a digital image of the fingerprint of the record holder, a field
308 for example comprises a digital image of an iris scan of the
record holder, and a field 310 for example comprises a digital
image of the signature of the record holder. Fields 304, 306,
308 and 310 may additionally or alternatively store biometric
templates, generated based on the corresponding images. Of
course, in alternative examples of the biometric database 208,
only some of these fields may be present and/or addition fields
comprising other biometric dAta could be included.
In the example of Figure 3, not all records comprise a
sample in each field 304 to 310. For example, some of the record
holders may not have provided some of the reference samples. In
particular, only records 0001 and 0002 comprise images of the
face of the record holder in field 304, labelled "imagelA" and
"image 2A" respectively. Furthermore, only records 0002 and 0003
comprise fingerprint images in field 306, labelled "image22" and
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"image3B" respectively, and only records 0001 and 0003 comprise
iris scan images in field 308, labelled "imagelC" and "image3C"
respectively. All three records comprise signature images in
field 310, labelled "imagelD", "image2D" and "image3D"
respectively.
A field 312 for example indicates which source
apparatuses the record holder is registered to use. In the
example of Figure 3, the record holders of records "0001" and
"0003" are registered with source apparatuses A and B
respectively, which correspond to the source apparatuses 204A
and 204B in Figure 2A respectively. The record holder of record
"0002" is registered with both source apparatuses A and B.
While in Figure 3 an example of the common biometric
database 208 is illustrated in which a single group of records
contains those of record holders registered with each of the
source apparatuses 204A, 204B, in alternative embodiments, the
common database 208 could be partitioned such that there is a
separate portion of the database corresponding to each source
apparatus, and users registered with more than one source
apparatus appear in each corresponding database partition.
Figure 4 is a flow diagram illustrating operations in a
method of identifying a biometric record according to an example
embodiment.
In an operation 401, a biometric input sample is
received from one of the biometric capturing devices 206A, 206B
of source apparatuses 204A, 204B. The input biometric sample is
for example transmitted along with source information indicating
from which of the source apparatuses 204A, 204B it originates.
In a subsequent operation 402, the reference table 210
of Figure 2B is used to identify and select, based on the source
information, at least the particular matching process to be
applied to the input biometric sample. This operation may also
involve determining from the reference table 210 the processing
resources to be assigned to this matching task. The processing
resources may be inherently defined by the matching process that
has been selected, as a function of the particular matching
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operations to be applied. Alternatively or additionally, a
priority level and/or a given number of processors is/are
assigned to the particular matching task, based on the source
information. For example, the processor block 220 of Figure 2B
comprises multiple processors and one or more of these
processors may be assigned to each matching process.
Furthermore, the processor time of the one or more of the
processors 220 is for example shared between the matching tasks
to be performed, and the higher the priority, the greater the
duration of the timeslots that are allocated to the
corresponding task.
In a next operation 403, the selected matching process
is applied to identify, among the records of the common database
associated with the source apparatus, a record having a
reference biometric sample matching the input biometric sample.
Figure 5 schematically illustrates the biometric
identification system 200 of Figure 2A in more detail according
to one particular example.
In Figure 5, the biometric identification system 200
comprises remote apparatus 502, an identity services framework
module 504, memory banks 506, and a payment module 508.
The remote apparatus 502 includes the source apparatus
204A, which in this example is a merchant payment terminal
comprising the biometric capture device 206A and a cashier
terminal application implementing the receiving equipment 212A.
The source apparatus 204B (not illustrated in Figure 5) is for
example of the same type, but corresponds to a different
merchant. The remote apparatus 502 also comprises a management
reporting module 509, which for example generates performance
statistics, a pre-enrolment web portal 510 implementing a
customer support interface 512, and an enrolment kiosk 514
implementing an enrolment application 516.
The identity services framework comprises the reference
table 210, as well as an event logging and reporting database
518, which for example stores event data used by the management
reporting module 509.
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The memory banks 506 include a biometric matching
module comprising the common biometric database 208, storing the
biometric samples, for example in the form of templates. The
memory banks 506 also include a biographic database 520, for
example storing personal details of record holders and
optionally storing the biometric images, based on which the
biometric templates may be generated. The memory banks 506 also
include a service data block 522 comprising a lookup table
linking biometric records to payment information, as will be
described in more detail below.
The payment module 508 includes a number of
applications supporting various payment schemes, in this example
direct debit and prepaid payment schemes. The direct debit
payment scheme involves the use of a financial administration
package 524 that controls the execution of payment requests.
Package 524 for example communicates with a direct debit
collection module 526, allowing payments to be debited directly
from a user bank account, and also with the collecting bank 528,
which is the bank associated with the source apparatus that is
to receive the funds. The direct connection (pre-paid) payment
scheme involves the use of a direct connection collection module
534, that communicates with the acquiring bank 536 receiving the
funds and with the issuing bank 538 supplying the funds from the
account of the user.
Operation of the biometric identification system 200 of
Figure 5 will now described in more detail with reference to
Figures 6A and 6B.
Figure 6A is a flow diagram showing an example of
operations performed for registering a new user to use one or
more of the source apparatuses 204A, 2043 to access products or
services.
In a first operation 601, a user enters biographical
information and banking data, for example via an intetilet
webpage using the customer support interface 512 of the pre-
enrolment web portal 510 of Figure 5. The user may also indicate
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the service or services that he/she wishes to be able to access
using one or more biometric samples.
In a next operation 602, the data entered in operation
601 is sent to the biometric identification unit 202.
In a next operation 603, a record, for example having
an identifier "ID123", is generated in the biographic database
520, and the users biographic data is stored in this record.
In a next operation 604, the record identifier "ID123
is sent to the payment module 508, where the banking data is
verified, and a corresponding record is created containing the
banking data, for example having the reference "IDABC".
In a next operation 607, the lookup table 522 is
modified to link together identifiers "ID123" and "IDABC" as
corresponding to a same user.
In a next operation 608, the identifier "ID123" of the
biographic record is sent to the user via the pre-enrolment web
portal 510.
In a next operation 609, the user is for example
invited to present himself/herself at the enrolment kiosk 514,
where one or more biometric capturing devices is available. The
identifier ID123 is entered, and the user's identity is for
example manually verified based on one or more identity
documents such as an ID card or passport.
In a next operation 610, one or more biometric
reference samples of the user are captured, and transmitted in a
subsequent operation 611 to the biometric identification unit
202.
In a next operation 612, templates are created and
deduplication is performed. This step for example involves
generating, based on the biometric image or images, templates
for use during the subsequent biometric process. Deduplication
then for example involves performing a matching process based on
the new templates, and verifying that there are no high scoring
records already in the database, which could indication that the
same person has already been registered.
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In a next operation 613, a new record, for example
having an identifier "ID741", is created, and in a subsequent
operation 614, the previously created record ID123 is replaced
by record ID741, which additionally stores the captured
reference biometrics. The lookup table 522 is updated
accordingly. Such a manipulation of identifiers is for example
performed to provide improved data privacy between databases of
the matching system.
Figure 63 is a flow diagram showing an example of
subsequent operations for initiating and executing an
identification request for a user, once the user has enrolled to
use one or more source apparatuses.
In an operation 615, items in a shop or supermarket are
scanned and the total bill is generated by the cashier terminal
application 212A.
In a next operation 616, the customer opts to pay using
biometric identification, and an input biometric sample is
captured by the capturing device 206A.
In a next operation 617, the biometric sample, activity
and source infoLmation, and the payment amount are transmitted
to the biometric identification system. The activity information
for example indicates that a payment is to be made, and the
source information for example identifies the source apparatus
or merchant.
In a next operation 618, a matching process is selected
and performed, as described above with reference to Figure 4. A
matching record, in this example the record with identifier
"ID741", is identified.
In a next operation 619, the lookup table 522 is used
to identify the banking record associated with the user, in this
example record IDABC.
In a next operation 620, the banking data associated
with record IDABC is used to initiate a payment of the
corresponding amount from the user to the bank account of the
merchant, and in an operation 621 a confilmation is transmitted
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to the merchant that the payment has been authorised, such that
the products can he released.
Figure 7 is a flow diagram showing examples of
operations in a biometric matching process applied by the
biometric identification unit 202 of Figure 2A, which is for
example implemented by the processing devices of Figure 2B.
In an operation 701, the biometric identification unit
202 loads the input biometric sample and some or all of the
reference biometric data samples ready for comparison. For
example, the samples are loaded into a memory device of the
biometric identification unit 202.
In a subsequent operation 702, a first filtering
operation is used to filter the records associated with the
identified source apparatus. This is achieved based on a
comparison of the input biometric sample with a corresponding
reference sample of each record, to filter out at least some of
the records. The first filtering operation is for example chosen
to filter out around 50 percent of the records very quickly and
with relatively few false negatives. The filter is for example
associated with a threshold that determines the records to be
filtered in or filtered out, based on a similarity score
determined for the record. A permissive threshold, for example
relatively low, can be chosen such that a relatively high number
of records are filtered in. A restrictive threshold, for example
relatively high, can be chosen such that a relatively high
number of records are filtered out. Generally, the threshold is
chosen such that, while there may be a high likelihood that a
non-matching record is filtered in, there is a very low risk
that the matching record is erroneously filtered out during this
operation.
As an example, assuming that during the first filtering
operation a similarity score of the input biometric sample with
each record is provided on a scale of 1 to 100, it may be
determined that any record scoring less than 50 can be filtered
out as it is very unlikely that such a low-scoring record
corresponds to a match. Of course, it may be quite likely that a
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lot of non-matching records score over such a threshold, but the
aim of the first filter is for example to reduce the number or
records as much as possible.
In a subsequent operation 703, a second filtering
operation is used to analyse the remaining records to identify,
if present, the record with a matching biometric sample. The
comparison performed in this operation may or not be based on
the same input biometric sample as used in the first filtering
operation 702. The second filtering operation is for example
chosen to have very low false negative and false positive
results, and is thus for example slower to execute per record
than the first filtering operation. For example, this second
filtering operation returns just one or a few of the best
matching records. In one example, a similarity score determined
in the second filtering operation is provided on a scale of 1 to
100, and a match is only considered if the record reaches a
score of at least 90, which is for example known to be quite
rare in the case of a non-matching record.
In a subsequent operation 704, it is determined whether
or not a matching record has been identified. For example, in
some embodiments, the second filtering operation 703 only
indicates a match if there is relatively high certainty. In
alternative embodiments, the second filtering operation 703
always outputs the best matching record, and also indicates the
level of certainty that the record is a match, i.e. that the
input biometric data sample came from the record holder. In such
an embodiment, operation 704 may involve comparing the level of
certainty with a threshold in order to judge whether there is a
match. For example, a match is considered to have occurred if
there is at least 99 percent certainty. Alternatively, the
certainty level of the matching record from each database
partition could be used to select the best matching record.
After operation 704, if a match was found from the
partition being processed, the next operation is 705, in which
this result is output. In particular, the score of each record
is for example compared to a threshold score, and if this
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threshold is exceeded, the record is considered to be a match.
If no record is found to have a score exceeding the threshold,
then there will be no match. If more than one record is found to
have a score exceeding the threshold, then an alert is for
example generated, and a manual verification is performed.
The flow diagram of Figure 7 provides just one example
of a type of matching process that could be applied in order to
find a record in the database 208 having one or more reference
biometric samples matching one or more input biometric samples.
There are various features of this process that can be modified
to provide alternative matching processes, such as the
particular filtering operations 702 and 703, the thresholds
applied in the operation 702, and the type of biometric data
sample compared in operations 702, 703, which could be the same
or different. Furthermore, rather than the two-phase approach
involving the first and second filtering operations, alternative
matching processes could involve applying only the second
filtering operation, or an additional filtering operations after
the second filtering operation, the final filtering operation
for example reducing the number of records to just one, or
indicating the best matches. The particular matching process to
be applied for an input biometric sample from a given source
apparatus is determined by the reference table 210 described
above.
The particular techniques used to compare the biometric
samples and detect a match will be known to those skilled in the
art, and are for example based on cascaded tests. For example,
fingerprint and face recognition is discussed in the publication
"Intelligent Biometric Techniques in Fingerprint and Face
Recognition", Jain, L.C. et al. and "Partially Parallel
Architecture for AdaBoost-Based Detection With Haar-like
Features", Hiromote et al.
Figures 8A and 8B respectively illustrate examples of
metadata extracted from database records and from biometric
input samples, which are for example used to determine a quality
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value of either or both of the samples to be compared. Such a
quality value is for used to deteLmine a difficulty score,
indicating the difficulty of accurately identifying a match or
non-match for the pair of records.
With reference first to Figure 8A, a table 800 shows in
rows some examples of metadata that may be extracted from a few
of the biometric reference samples of database portion 300 of
Figure 3. For example, for the photo of face "imagelA" of record
11000111, the metadata for example comprises the age and gender of
the record holder, in this example having the respective values
66 and male. This information is for example obtained from the
record holder.
Furthermore, data relating to the image may be
extracted.
For example the image size may be extracted as the
number of pixels in the rectangular image, which in the example
of "imagelA" is for example 1200 by 1600 pixels.
A sharpness level may be evaluated on a scale of 1 to
10 using techniques known to those skilled in the art, which in
the example of "imagelA" is equal to 8.
A viewing angle may be determined, a zero angle for
example indicating that the face is head-on to the camera, a
positive angle indicating a face turned to the right, and a
negative angle indicating a face turned to the left. In the
example of "imagelA", the angle is for example 5 degrees.
A contrast level, for example on a scale of 1 to 20,
may also be evaluated by techniques that will be known to those
skilled in the art. In the example of "imagelA", the value is
11.
It will be apparent to those skilled in the art that
only some of these values, and/or additional values, could be
extracted from the database samples. Furthermore, metadata
indicating the particular types of biometric reference samples
present in each record is for example extracted.
An overall quality score may be determined for each
record, for example on a scale of 0 to 10, indicating an overall
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quality rating of the biometric sample based on the various
parameters available. An example of this score is shown in the
right-hand column of table 800.
With reference to Figure 8B, a table 850 shows an
example of metadata that could be extracted from two input
biometric samples "input1A" and "input1B", which are for example
a face image and a fingerprint respectively. In this example,
the extracted metadata is for example the image size, the image
sharpness, the viewing angle and an overall quality score, which
are determined using the same criteria as described above in
relation to Figure 8A.
The difficulty score is for example evaluated for each
input biometric sample, or for each comparison to be performed.
As an example, if an input biometric sample is to be compared to
50 reference biometric samples of the biometric database, a
difficulty score may be determined for each of these 50
comparisons based on a quality value of either or both of the
samples that are compared. The quality score may be determined
based any one or combination of the quality values shown in the
columns of tables 800 and 850 of Figures 8A and 8B. In one
example, a quality score of a pair of records is evaluated by
multiplying the overall quality score for each record, or by
subtracting one of the viewing angles from the other to
determine a difference in the viewing angles.
The difficulty scores that have been determined may be
used for billing purposes, and/or to adjust the thresholds used
during the matching process such that a target performance can
be achieved.
A feature of the example embodiments described herein is
that, by selecting a matching process to be applied based on source
information, the use of the processing resources of the biometric
identification unit, and thus the processing time, may be optimized
for each source apparatus.
While a number of specific embodiments of devices and
methods of the present disclosure have been provided above, it will
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be apparent to those skilled in the art that various modifications
and alternatives could be applied.
For example, it will be apparent to those skilled in the
art that the examples of matching processes applied to the records
are merely a few such examples, and that other matching processes
could be used.
Embodiments of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer programs,
i.e., one or more modules of computer program instructions, encoded
on computer storage medium for execution by, or to control the
operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially-generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal, which is generated
to encode information for transmission to suitable receiver
apparatus for execution by a data processing apparatus. A computer
storage medium can be, or be included in, a computer-readable
storage device, a computer-readable storage substrate, a random or
serial access memory array or device, or a combination of one or
more of them. Moreover, while a computer storage medium is not a
propagated signal, a computer storage medium can be a source or
destination of computer program instructions encoded in an
artificially-generated propagated signal. The computer storage
medium can also be, or be included in, one or more separate physical
components or media (e.g., multiple CDs, disks, or other storage
devices).
The operations described in this specification can be
implemented as operations performed by a data processing apparatus
on data stored on one or more computer-readable storage devices or
received from other sources.
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The term "data processing apparatus" encompasses all kinds
of apparatus, devices, and machines for processing data, including
by way of example a programmable processor, a computer, a system on
a chip, or multiple ones, or combinations, of the foregoing The
apparatus can include special purpose logic circuitry, e.g., an FPGA
(field programmable gate array) or an ASTC (application-specific
integrated circuit). The apparatus can also include, in addition to
hardware, code that creates an execution environment for the
computer program in question, e.g., code that constitutes processor
firmware, a protocol stack, a database management system, an
operating system, a cross-platform runtime environment, a virtual
machine, or a combination of one or more of them. The apparatus and
execution environment can realize various different computing model
infrastructures, such as web services, distributed computing and
grid computing infrastructures.
A computer program (also known as a program, software,
software application, script, or code) can be written in any form of
programming language, including compiled or interpreted languages,
declarative or procedural languages, and it can be deployed in any
form, including as a stand-alone program or as a module, component,
subroutine, object, or other unit suitable for use in a computing
environment. A computer program may, but need not, correspond to a
file in a file system. A program can be stored in a portion of a
file that holds other programs or data (e.g., one or more scripts
stored in a markup language document), in a single file dedicated to
the program in question, or in multiple coordinated files (e.g.,
files that store one or more modules, sub-programs, or portions of
code). A computer program can be deployed to be executed on one
computer or on multiple computers that are located at one site or
distributed across multiple sites and interconnected by a
communication network.
The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
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can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC
(application-specific integrated circuit).
Processors suitable for the execution of a computer
program include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, a processor will receive instructions
and data from a read-only memory or a random access memory or both.
The essential elements of a computer are a processor for performing
actions in accordance with instructions and one or more memory
devices for storing instructions and data. Generally, a computer
will also include, or be operatively coupled to receive data from or
transfer data to, or both, one or more mass storage devices for
storing data, e.g., magnetic, magneto-optical disks, or optical
disks. However, a computer need not have such devices. Moreover, a
computer can he embedded in another device, e.g., a mobile
telephone, a personal digital assistant (PDA), a mobile audio or
video player, a game console, a Global Positioning System (GPS)
receiver, or a portable storage device (e.g., a universal serial bus
(USB) flash drive), to name just a few. Devices suitable for storing
computer program instructions and data include all forms of
non-volatile memory, media and memory devices, including by way of
example semiconductor memory devices, e.g., EPROM, EEPROM, and flash
memory devices; magnetic disks, e.g., internal hard disks or
removable disks; magneto-optical disks; and CD-ROM and DVD-ROM
disks. The processor and the memory can be supplemented by, or
incorporated in, special purpose logic circuitry.
To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented on
a computer having a display device, e.g., a CRT (cathode ray tube)
or LCD (liquid crystal display) monitor, for displaying information
to the user and a keyboard and a pointing device, e.g., a mouse or a
trackball, by which the user can provide input to the computer.
Other kinds of devices can be used to provide for interaction with a
user as well; for example, feedback provided to the user can be any
form of sensory feedback, e.g., visual feedback, auditory feedback,
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or tactile feedback; and input from the user can be received in any
form, including acoustic, speech, or tactile input. In addition, a
computer can interact with a user by sending documents to and
receiving documents from a device that is used by the user; for
example, by sending web pages to a web browser on a user's client
device in response to requests received from the web browser.
Embodiments of the subject matter described in this
specification can be implemented in a computing system that includes
a back-end component, e.g., as a data server, or that includes a
middleware component, e.g., an application server, or that includes
a front-end component, e.g., a client computer having a graphical
user interface or a Web browser through which a user can interact
with an implementation of the subject matter described in this
specification, or any combination of one or more such back-end,
middleware, or front-end components. The components of the system
can be interconnected by any form or medium of digital data
communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), an inter-network (e.g., the Internet),
and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
A system of one or more computers can be configured to
perform particular operations or actions by virtue of having
software, firmware, hardware, or a combination of them installed on
the system that in operation causes or cause the system to perform
the actions. One or more computer programs can be configured to
perform particular operations or actions by virtue of including
instructions that, when executed by data processing apparatus, cause
the apparatus to perform the actions.
The computing system can include clients and servers. A
client and server are generally remote from each other and typically
interact through a communication network. The relationship of client
and server arises by virtue of computer programs running on the
respective computers and having a client-server relationship to each
other. In some embodiments, a server transmits data (e.g., an HTML
page) to a client device (e.g., for purposes of displaying data to
and receiving user input from a user interacting with the client
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device). Data generated at the client device (e.g., a result of the
user interaction) can be received flum the client device at the
server.
While this specification contains many specific
implementation details, these should not be construed as limitations
on the scope of any inventions or of what may be claimed, but rather
as descriptions of features specific to particular embodiments of
particular inventions. Certain features that are described in this
specification in the context of separate embodiments can also be
implemented in combination in a single embodiment. Conversely,
various features that are described in the context of a single
embodiment can also be implemented in multiple embodiments
separately or in any suitable sub-combination. Moreover, although
features may be described above as acting in certain combinations
and even initially claimed as such, one or more features from a
claimed combination can in some cases be excised from the
combination, and the claimed combination may be directed to a sub-
combination or variation of a sub-combination.
Similarly, while operations are depicted in the drawings
in a particular order, this should not be understood as requiring
that such operations be performed in the particular order shown or
in sequential order, or that all illustrated operations be
performed, to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems can generally be integrated
together in a single software product or packaged into multiple
software products.
Thus, particular embodiments of the subject matter have
been described. Other embodiments are within the scope of the
following claims. In some cases, the actions recited in the claims
can be performed in a different order and still achieve desirable
results. In addition, the processes depicted in the accompanying
figures do not necessarily require the particular order shown, or
, CA 02792688 2012-10-17
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sequential order, to achieve desirable results. In certain
implementations, multitasking and parallel processing may be
advantageous.
